{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2H6YLCPOXvZ",
        "outputId": "3edf19f1-2009-40fa-bb04-ca3146bdf20c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting datasets\n",
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            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
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            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
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            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n",
            "Successfully installed accelerate-0.29.3 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install numpy\n",
        "!pip install pandas\n",
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H-LANn-hUlZh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "np.random.seed(11)\n",
        "import torch\n",
        "torch.manual_seed(11)\n",
        "import random\n",
        "random.seed(11)\n",
        "\n",
        "import transformers\n",
        "import pandas as pd\n",
        "\n",
        "from datasets import load_metric\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import Trainer\n",
        "from transformers import TrainingArguments\n",
        "from transformers import RobertaForSequenceClassification\n",
        "from transformers import RobertaTokenizerFast\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 16FSh45xge5RybunR6-4tER8Vi_pXXx4a # download the entire dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IM--HAJWVJ0D",
        "outputId": "c9a8a637-b78e-4ca9-d12c-3813f74d77ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=16FSh45xge5RybunR6-4tER8Vi_pXXx4a\n",
            "To: /content/German_French_UK_China_MENA_040420.csv\n",
            "\r  0% 0.00/2.88M [00:00<?, ?B/s]\r100% 2.88M/2.88M [00:00<00:00, 274MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label2num = {\"Monitoring environmental impact\": 1,\n",
        "              \"Preventing pollution\": 2,\n",
        "              \"Strengthening ecosystems\": 3,\n",
        "              \"Reducing use\": 4,\n",
        "              \"Reusing\": 5,\n",
        "              \"Recycling\": 6,\n",
        "              \"Repurposing\":7,\n",
        "              \"Encouraging and supporting others\": 8,\n",
        "              \"Educating and training for sustainability\": 9,\n",
        "              \"Creating sustainable products and processes\": 10,\n",
        "              \"Embracing innovation for sustainability\": 11,\n",
        "              \"Changing how work is done\": 12,\n",
        "              \"Choosing responsible alternatives\": 13,\n",
        "              \"Instituting programs and policies\": 14,\n",
        "              \"Others\":15}\n",
        "def datasetclearning(df):\n",
        "  # Select two relevant columns from the original dataset\n",
        "  df = df[['Major_Industry','description_behavior', 'consequences','Specific_cat']]\n",
        "  df['description_behavior'] = df['description_behavior']+' '+df['consequences']\n",
        "  # Collapse the data for the NLP paper\n",
        "  other = [ \"Putting environmental interests first\", \"Lobbying and activism\"]\n",
        "  # Collapse \"Putting environmental interest first\" and \"Lobbying and Activism\" into \"the Other\" category\n",
        "  replacement = df['Specific_cat'].where(df['Specific_cat'].isin(other) == False, \"Others\")\n",
        "  df = pd.concat([df['description_behavior'], replacement], axis =1)\n",
        "  for key, val in label2num.items():\n",
        "    label2num[key] = val-1\n",
        "  num2label = {y:x for x,y in label2num.items()}\n",
        "  # Convert text label to number numeric\n",
        "  df_label = df['Specific_cat'].map(label2num)\n",
        "  df_label.name = 'label'\n",
        "  df = pd.concat([df,df_label], axis = 1)\n",
        "  # Retain only the behaviors and numberic label\n",
        "  df = df[['description_behavior','label']]\n",
        "  # Rename the columns of output dataframe\n",
        "  df.columns = ['text', 'label']\n",
        "  # Return the dataframe\n",
        "  return [df, num2label]"
      ],
      "metadata": {
        "id": "zJxbuq8tPRZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = RobertaTokenizerFast.from_pretrained('roberta-large')\n",
        "mlength = 512\n",
        "\n",
        "df = pd.read_csv(\"/content/German_French_UK_China_MENA_040420.csv\")\n",
        "df, num2label = datasetclearning(df)\n",
        "\n",
        "def test_train_split(df, seed):\n",
        "  X_train, X_test, Y_train, Y_test, indices_train, indices_test = train_test_split(df['text'], df['label'], df.index, test_size=2449, random_state = seed, stratify = df['label'])\n",
        "  return [X_train, X_test, Y_train, Y_test]"
      ],
      "metadata": {
        "id": "hTL4dINeVkI3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
          "referenced_widgets": [
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          ]
        },
        "outputId": "0156550d-7a68-4d2d-89bf-53f26833702c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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              "version_major": 2,
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        {
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          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e14e4a10f6d454b977b91b15883511a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-207cd080be09>:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['description_behavior'] = df['description_behavior']+' '+df['consequences']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class PsyDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)"
      ],
      "metadata": {
        "id": "s9TlhqaVZmgP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# max([len(i) for i in list(df[\"text\"])]) # 517\n",
        "\n",
        "def data_prep(df, seed):\n",
        "  # 1. Test-train split (Test set has 2449 cases across simulation conditions)\n",
        "  X_train, X_test, Y_train, Y_test = test_train_split(df, seed)\n",
        "\n",
        "  # 2. Dev-train split (Dev set has 1225 cases across simulation conditions)\n",
        "  X_train, X_dev, Y_train, Y_dev = train_test_split(X_train, Y_train, test_size=1225, random_state = seed, stratify = Y_train)\n",
        "\n",
        "  # print(len(X_train), len(X_dev), len(X_test))\n",
        "  # print(\"# classes in train\", len(set(Y_train)))\n",
        "  # print(\"# classes in dev\", len(set(Y_dev)))\n",
        "  # print(\"# classes in test\", len(set(Y_test)))\n",
        "\n",
        "  X_train = list(X_train)\n",
        "  X_dev = list(X_dev)\n",
        "  X_test = list(X_test)\n",
        "\n",
        "  Y_train = list(Y_train)\n",
        "  Y_dev = list(Y_dev)\n",
        "  Y_test = list(Y_test)\n",
        "\n",
        "  train_encodings = tokenizer(X_train, truncation=True, padding=True, max_length=mlength)\n",
        "  dev_encodings = tokenizer(X_dev, truncation=True, padding=True, max_length = mlength)\n",
        "  test_encodings = tokenizer(X_test, truncation=True, padding=True, max_length= mlength)\n",
        "\n",
        "  train_dataset = PsyDataset(train_encodings, Y_train)\n",
        "  dev_dataset = PsyDataset(dev_encodings, Y_dev)\n",
        "  test_dataset = PsyDataset(test_encodings, Y_test)\n",
        "  return train_dataset, dev_dataset, test_dataset"
      ],
      "metadata": {
        "id": "JmbJrfELfs9G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        },
        "outputId": "77cf5f83-d0ee-4a1d-981b-60c20a146cc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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              "model_id": "09c0f74c4db84d2fa711882f71b06835"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acb9824238934d66ac2c88e7825cb82a"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nRKm_KMi4d5Y"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    metric1 = load_metric(\"precision\", trust_remote_code=True)\n",
        "    metric2 = load_metric(\"recall\", trust_remote_code=True)\n",
        "    metric3 = load_metric(\"f1\", trust_remote_code=True)\n",
        "\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "    total = len(labels)\n",
        "    avg_precision = 0\n",
        "    avg_recall = 0\n",
        "    avg_f1 = 0\n",
        "\n",
        "    for i in range(15):\n",
        "      weight = (labels == i).sum() / total\n",
        "      precision = metric1.compute(predictions=predictions, references=labels, labels=[i], average='micro')[\"precision\"]\n",
        "      recall = metric2.compute(predictions=predictions, references=labels, labels=[i], average='micro')[\"recall\"]\n",
        "      f1 = metric3.compute(predictions=predictions, references=labels, labels=[i], average='micro')[\"f1\"]\n",
        "      avg_precision += weight * precision\n",
        "      avg_recall += weight * recall\n",
        "      avg_f1 += weight * f1\n",
        "\n",
        "    return {\"precision\": avg_precision, \"recall\": avg_recall, \"f1\": avg_f1}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./AMPPS-results',          # output directory\n",
        "    num_train_epochs=10,             # total number of training epochs\n",
        "    per_device_train_batch_size=64,  # batch size per device during training\n",
        "    per_device_eval_batch_size=128,   # batch size for evaluation\n",
        "    warmup_steps=0,                  # number of warmup steps for learning rate scheduler\n",
        "    weight_decay=0.01,               # strength of weight decay\n",
        "    logging_dir='./logs',            # directory for storing logs\n",
        "    logging_steps=100,\n",
        "    learning_rate = 1e-5,\n",
        "    save_strategy= \"epoch\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    load_best_model_at_end= True,\n",
        "    metric_for_best_model=\"precision\",\n",
        "    seed = 11,\n",
        ")\n",
        "\n",
        "def model_init():\n",
        "    return RobertaForSequenceClassification.from_pretrained(\"roberta-large\", num_labels=15)"
      ],
      "metadata": {
        "id": "KyOxYddvgFkP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_fVi9IbRDSgB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "637b1d3a-ff94-4422-f82f-0f413e20670b"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='960' max='960' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [960/960 17:39, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.360176</td>\n",
              "      <td>0.593579</td>\n",
              "      <td>0.605714</td>\n",
              "      <td>0.571513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.171100</td>\n",
              "      <td>0.960665</td>\n",
              "      <td>0.698686</td>\n",
              "      <td>0.704490</td>\n",
              "      <td>0.690309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.131800</td>\n",
              "      <td>0.893033</td>\n",
              "      <td>0.721007</td>\n",
              "      <td>0.724898</td>\n",
              "      <td>0.718473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.828500</td>\n",
              "      <td>0.915083</td>\n",
              "      <td>0.729444</td>\n",
              "      <td>0.722449</td>\n",
              "      <td>0.716955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.678900</td>\n",
              "      <td>0.897546</td>\n",
              "      <td>0.740762</td>\n",
              "      <td>0.734694</td>\n",
              "      <td>0.732833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.537800</td>\n",
              "      <td>0.931500</td>\n",
              "      <td>0.740855</td>\n",
              "      <td>0.737959</td>\n",
              "      <td>0.735786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.445500</td>\n",
              "      <td>0.946156</td>\n",
              "      <td>0.736794</td>\n",
              "      <td>0.732245</td>\n",
              "      <td>0.731484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.378200</td>\n",
              "      <td>0.978606</td>\n",
              "      <td>0.744600</td>\n",
              "      <td>0.737959</td>\n",
              "      <td>0.737615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.315100</td>\n",
              "      <td>0.970496</td>\n",
              "      <td>0.737393</td>\n",
              "      <td>0.732245</td>\n",
              "      <td>0.732782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.277500</td>\n",
              "      <td>0.979328</td>\n",
              "      <td>0.733614</td>\n",
              "      <td>0.733061</td>\n",
              "      <td>0.731434</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
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        },
        {
          "metadata": {
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          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='960' max='960' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [960/960 17:41, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.242824</td>\n",
              "      <td>0.602868</td>\n",
              "      <td>0.621224</td>\n",
              "      <td>0.589929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.996900</td>\n",
              "      <td>0.965878</td>\n",
              "      <td>0.699931</td>\n",
              "      <td>0.701224</td>\n",
              "      <td>0.689402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.066500</td>\n",
              "      <td>0.898165</td>\n",
              "      <td>0.723737</td>\n",
              "      <td>0.724082</td>\n",
              "      <td>0.718770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.792100</td>\n",
              "      <td>0.891369</td>\n",
              "      <td>0.723960</td>\n",
              "      <td>0.724082</td>\n",
              "      <td>0.720970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.625100</td>\n",
              "      <td>0.924999</td>\n",
              "      <td>0.731834</td>\n",
              "      <td>0.729796</td>\n",
              "      <td>0.728109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.498500</td>\n",
              "      <td>0.938948</td>\n",
              "      <td>0.727319</td>\n",
              "      <td>0.724898</td>\n",
              "      <td>0.724412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.402800</td>\n",
              "      <td>0.962109</td>\n",
              "      <td>0.736721</td>\n",
              "      <td>0.735510</td>\n",
              "      <td>0.735376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.353900</td>\n",
              "      <td>0.985369</td>\n",
              "      <td>0.730730</td>\n",
              "      <td>0.730612</td>\n",
              "      <td>0.729216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.276900</td>\n",
              "      <td>1.008979</td>\n",
              "      <td>0.734410</td>\n",
              "      <td>0.733061</td>\n",
              "      <td>0.732111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.246300</td>\n",
              "      <td>1.008651</td>\n",
              "      <td>0.735207</td>\n",
              "      <td>0.735510</td>\n",
              "      <td>0.734157</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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            ]
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        },
        {
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          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='960' max='960' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [960/960 17:24, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.299998</td>\n",
              "      <td>0.550793</td>\n",
              "      <td>0.596735</td>\n",
              "      <td>0.561503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.046300</td>\n",
              "      <td>0.998990</td>\n",
              "      <td>0.681534</td>\n",
              "      <td>0.680816</td>\n",
              "      <td>0.667311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.100700</td>\n",
              "      <td>0.943175</td>\n",
              "      <td>0.722225</td>\n",
              "      <td>0.721633</td>\n",
              "      <td>0.716704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.816200</td>\n",
              "      <td>0.934766</td>\n",
              "      <td>0.728668</td>\n",
              "      <td>0.723265</td>\n",
              "      <td>0.720005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.650700</td>\n",
              "      <td>0.912119</td>\n",
              "      <td>0.733022</td>\n",
              "      <td>0.733061</td>\n",
              "      <td>0.731144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.523600</td>\n",
              "      <td>0.953100</td>\n",
              "      <td>0.735747</td>\n",
              "      <td>0.737143</td>\n",
              "      <td>0.734106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.426200</td>\n",
              "      <td>0.975348</td>\n",
              "      <td>0.729982</td>\n",
              "      <td>0.733061</td>\n",
              "      <td>0.727012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.362400</td>\n",
              "      <td>1.001911</td>\n",
              "      <td>0.736512</td>\n",
              "      <td>0.737959</td>\n",
              "      <td>0.734320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.299900</td>\n",
              "      <td>1.001293</td>\n",
              "      <td>0.732937</td>\n",
              "      <td>0.735510</td>\n",
              "      <td>0.732373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.259600</td>\n",
              "      <td>1.009228</td>\n",
              "      <td>0.735272</td>\n",
              "      <td>0.737143</td>\n",
              "      <td>0.734562</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='241' max='960' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [241/960 03:16 < 09:51, 1.22 it/s, Epoch 2.50/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.521049</td>\n",
              "      <td>0.561683</td>\n",
              "      <td>0.527347</td>\n",
              "      <td>0.482611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.283800</td>\n",
              "      <td>1.032879</td>\n",
              "      <td>0.666943</td>\n",
              "      <td>0.663673</td>\n",
              "      <td>0.646710</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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        },
        {
          "metadata": {
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          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='960' max='960' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [960/960 13:42, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.521049</td>\n",
              "      <td>0.561683</td>\n",
              "      <td>0.527347</td>\n",
              "      <td>0.482611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.283800</td>\n",
              "      <td>1.032879</td>\n",
              "      <td>0.666943</td>\n",
              "      <td>0.663673</td>\n",
              "      <td>0.646710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.212200</td>\n",
              "      <td>0.906625</td>\n",
              "      <td>0.720780</td>\n",
              "      <td>0.711020</td>\n",
              "      <td>0.703702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.863000</td>\n",
              "      <td>0.879918</td>\n",
              "      <td>0.729529</td>\n",
              "      <td>0.727347</td>\n",
              "      <td>0.724385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.690600</td>\n",
              "      <td>0.887029</td>\n",
              "      <td>0.743405</td>\n",
              "      <td>0.739592</td>\n",
              "      <td>0.736350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.542500</td>\n",
              "      <td>0.910882</td>\n",
              "      <td>0.745361</td>\n",
              "      <td>0.741224</td>\n",
              "      <td>0.737513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.460800</td>\n",
              "      <td>0.898502</td>\n",
              "      <td>0.745120</td>\n",
              "      <td>0.742857</td>\n",
              "      <td>0.740965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.378600</td>\n",
              "      <td>0.925084</td>\n",
              "      <td>0.740752</td>\n",
              "      <td>0.737959</td>\n",
              "      <td>0.735785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.323900</td>\n",
              "      <td>0.943525</td>\n",
              "      <td>0.747615</td>\n",
              "      <td>0.746122</td>\n",
              "      <td>0.744982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.287000</td>\n",
              "      <td>0.944040</td>\n",
              "      <td>0.740849</td>\n",
              "      <td>0.740408</td>\n",
              "      <td>0.739090</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
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        },
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          },
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='960' max='960' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [960/960 14:48, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.414486</td>\n",
              "      <td>0.557921</td>\n",
              "      <td>0.559184</td>\n",
              "      <td>0.523944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.131700</td>\n",
              "      <td>1.029209</td>\n",
              "      <td>0.672807</td>\n",
              "      <td>0.670204</td>\n",
              "      <td>0.659384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.164800</td>\n",
              "      <td>0.888690</td>\n",
              "      <td>0.707367</td>\n",
              "      <td>0.706939</td>\n",
              "      <td>0.698886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.846100</td>\n",
              "      <td>0.888265</td>\n",
              "      <td>0.713278</td>\n",
              "      <td>0.708571</td>\n",
              "      <td>0.706152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.658000</td>\n",
              "      <td>0.882341</td>\n",
              "      <td>0.739991</td>\n",
              "      <td>0.728163</td>\n",
              "      <td>0.727958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.544100</td>\n",
              "      <td>0.882103</td>\n",
              "      <td>0.739265</td>\n",
              "      <td>0.733061</td>\n",
              "      <td>0.730585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.454900</td>\n",
              "      <td>0.901117</td>\n",
              "      <td>0.737164</td>\n",
              "      <td>0.726531</td>\n",
              "      <td>0.726255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.381100</td>\n",
              "      <td>0.914147</td>\n",
              "      <td>0.741596</td>\n",
              "      <td>0.735510</td>\n",
              "      <td>0.735320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.319500</td>\n",
              "      <td>0.925626</td>\n",
              "      <td>0.745275</td>\n",
              "      <td>0.739592</td>\n",
              "      <td>0.739416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.295200</td>\n",
              "      <td>0.923598</td>\n",
              "      <td>0.741889</td>\n",
              "      <td>0.740408</td>\n",
              "      <td>0.739436</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean precision 0.7393420277425556\n",
            "mean recall 0.7380155165373623\n",
            "mean f1 0.7371009095700296\n"
          ]
        }
      ],
      "source": [
        "precisions = []\n",
        "recalls = []\n",
        "f1s = []\n",
        "\n",
        "for seed in range(1, 6):\n",
        "  train_dataset, dev_dataset, test_dataset = data_prep(df, seed)\n",
        "  trainer = Trainer(\n",
        "      model_init=model_init,               # the instantiated 🤗 Transformers model to be trained\n",
        "      args=training_args,                  # training arguments, defined above\n",
        "      train_dataset=train_dataset,         # training dataset\n",
        "      eval_dataset=dev_dataset,            # evaluation dataset\n",
        "      compute_metrics=compute_metrics,     # compute_metrics\n",
        "      )\n",
        "  trainer.train()\n",
        "  output = trainer.predict(test_dataset)\n",
        "  metrics = classification_report(output.label_ids, np.argmax(output.predictions, axis=-1), output_dict = True)\n",
        "  precisions.append(metrics['weighted avg'].get('precision'))\n",
        "  recalls.append(metrics['weighted avg'].get('recall'))\n",
        "  f1s.append(metrics['weighted avg'].get('f1-score'))\n",
        "\n",
        "print(\"mean precision\", np.mean(precisions))\n",
        "print(\"mean recall\", np.mean(recalls))\n",
        "print(\"mean f1\", np.mean(f1s))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(precisions)\n",
        "print(recalls)\n",
        "print(f1s)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QNy0jq_-3cr",
        "outputId": "de82fca0-c6a7-460b-ef38-c22a84067ce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.7521898712971296, 0.740527460801228, 0.7268369471218904, 0.7293099080211554, 0.7478459514713749]\n",
            "[0.7484687627603104, 0.7386688444262964, 0.7256022866476113, 0.7292772560228665, 0.7480604328297265]\n",
            "[0.7474588331264028, 0.7389351913976763, 0.723818344200286, 0.7283635852980667, 0.7469285938277163]\n"
          ]
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